The application of self-supervision to speech representation learning has garnered significant interest in recent years, due to its scalability to large amounts of unlabeled data. However, much progress, both in terms of pre-training and downstream evaluation, has remained concentrated in monolingual models that only consider English. Few models consider other languages, and even fewer consider indigenous ones. In our submission to the New Language Track of the ASRU 2023 ML-SUPERB Challenge, we present an ASR corpus for Quechua, an indigenous South American Language. We benchmark the efficacy of large SSL models on Quechua, along with 6 other indigenous languages such as Guarani and Bribri, on low-resource ASR. Our results show surprisingly strong performance by state-of-the-art SSL models, showing the potential generalizability of large-scale models to real-world data.
According to interviews with people who work with speech impaired persons, speech impaired people have difficulties in communicating with other people around them who do not know the sign language, and this situation may cause them to isolate themselves from society and lose their sense of independence. With this paper, to increase the quality of life of individuals with facilitating communication between individuals who use sign language and who do not know this language, a new American Sign Language (ASL) digits dataset that can help to create machine learning algorithms which need to large …
University of Nairobi（内罗毕大学）；Maseno University（马塞诺大学）；Africa Nazarene University（非洲拿撒勒大学）
Building automatic speech recognition (ASR) systems is a challenging task, especially for under-resourced languages that need to construct corpora nearly from scratch and lack sufficient training data. It has emerged that several African indigenous languages, including Kiswahili, are technologically under-resourced. ASR systems are crucial, particularly for the hearing-impaired persons who can benefit from having transcripts in their native languages. However, the absence of transcribed speech datasets has complicated efforts to develop ASR models for these indigenous languages. This paper exp…
Université de Paris（巴黎大学）；CNRS（法国国家科学研究中心）；PSL Research University（PSL研究大学）；ENS（巴黎高等师范学院）；EHESS（法国高等社会科学研究院）；INRIA（法国国家信息与自动化研究所）
We present the Perceptimatic English Benchmark, an open experimental benchmark for evaluating quantitative models of speech perception in English. The benchmark consists of ABX stimuli along with the responses of 91 American English-speaking listeners. The stimuli test discrimination of a large number of English and French phonemic contrasts. They are extracted directly from corpora of read speech, making them appropriate for evaluating statistical acoustic models (such as those used in automatic speech recognition) trained on typical speech data sets. We show that phone discrimination is corr…
Indigenous languages of the American continent are highly diverse. However, they have received little attention from the technological perspective. In this paper, we review the research, the digital resources and the available NLP systems that focus on these languages. We present the main challenges and research questions that arise when distant languages and low-resource scenarios are faced. We would like to encourage NLP research in linguistically rich and diverse areas like the Americas.
Machine translation (MT) involving Indigenous languages, including those possibly endangered, is challenging due to lack of sufficient parallel data. We describe an approach exploiting bilingual and multilingual pretrained MT models in a transfer learning setting to translate from Spanish to ten South American Indigenous languages. Our models set new SOTA on five out of the ten language pairs we consider, even doubling performance on one of these five pairs. Unlike previous SOTA that perform data augmentation to enlarge the train sets, we retain the low-resource setting to test the effectivene…
Nara Institute of Science and Technology（奈良科学技术研究所）；RIKEN Center for Advanced Intelligence Project AIP（理化学研究所高级智能项目AIP中心）
Even though over seven hundred ethnic languages are spoken in Indonesia, the available technology remains limited that could support communication within indigenous communities as well as with people outside the villages. As a result, indigenous communities still face isolation due to cultural barriers; languages continue to disappear. To accelerate communication, speech-to-speech translation (S2ST) technology is one approach that can overcome language barriers. However, S2ST systems require machine translation (MT), speech recognition (ASR), and synthesis (TTS) that rely heavily on supervised…
Indian Institute of Science (IISc)（印度科学学院）；IIIT Hyderabad（印度海得拉巴国际信息技术学院）
In this study, listeners of varied Indian nativities are asked to listen and recognize TIMIT utterances spoken by American speakers. We have three kinds of responses from each listener while they recognize an utterance: 1. Sentence difficulty ratings, 2. Speaker difficulty ratings, and 3. Transcription of the utterance. From these transcriptions, word error rate (WER) is calculated and used as a metric to evaluate the similarity between the recognized and the original sentences.The sentences selected in this study are categorized into three groups: Easy, Medium and Hard, based on the frequency…
Aix-Marseille Université（艾克斯-马赛大学）；Université de Toulon（土伦大学）
We perform a large-scale analysis of language diatopic variation using geotagged microblogging datasets. By collecting all Twitter messages written in Spanish over more than two years, we build a corpus from which a carefully selected list of concepts allows us to characterize Spanish varieties on a global scale. A cluster analysis proves the existence of well defined macroregions sharing common lexical properties. Remarkably enough, we find that Spanish language is split into two superdialects, namely, an urban speech used across major American and Spanish citites and a diverse form that enco…
Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
DePaul University（德保罗大学）；University of Washington（华盛顿大学）；Carnegie Mellon University（卡内基梅隆大学）；Cornell Tech University（康奈尔科技大学）；University of Maryland（马里兰大学）
Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at…
Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English
Department of English Language and Linguistics, Institute of English and American Studies, Faculty of Arts and Humanities, Heinrich Heine University Düsseldorf, Germany（海因里希·海涅大学杜塞尔多夫分校，人文与艺术学院，英美研究学院，英语语言与语言学系）；Department of Linguistics, University of Florida, United States of America（佛罗里达大学语言学系）
In this paper, we explore automatic prediction of dialect density of the African American English (AAE) dialect, where dialect density is defined as the percentage of words in an utterance that contain characteristics of the non-standard dialect. We investigate several acoustic and language modeling features, including the commonly used X-vector representation and ComParE feature set, in addition to information extracted from ASR transcripts of the audio files and prosodic information. To address issues of limited labeled data, we use a weakly supervised model to project prosodic and X-vector …
Currently, natural language processing (NLP) models proliferate language discrimination leading to potentially harmful societal impacts as a result of biased outcomes. For example, part-of-speech taggers trained on Mainstream American English (MAE) produce non-interpretable results when applied to African American English (AAE) as a result of language features not seen during training. In this work, we incorporate a human-in-the-loop paradigm to gain a better understanding of AAE speakers' behavior and their language use, and highlight the need for dialectal language inclusivity so that native…
Probing for Phonology in Self-Supervised Speech Representations: A Case Study on Accent Perception
Department of Linguistics, University of Florida（语言学系，佛罗里达大学）；Department of English Language and Linguistics, Heinrich Heine University Düsseldorf（英语语言与语言学系，海因里希-海涅大学杜塞尔多夫）
Algorithms are widely applied to detect hate speech and abusive language in social media. We investigated whether the human-annotated data used to train these algorithms are biased. We utilized a publicly available annotated Twitter dataset (Founta et al. 2018) and classified the racial, gender, and party identification dimensions of 99,996 tweets. The results showed that African American tweets were up to 3.7 times more likely to be labeled as abusive, and African American male tweets were up to 77% more likely to be labeled as hateful compared to the others. These patterns were statistically…
Yale University（耶鲁大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
We train a diachronic long short-term memory (LSTM) part-of-speech tagger on a large corpus of American English from the 19th, 20th, and 21st centuries. We analyze the tagger's ability to implicitly learn temporal structure between years, and the extent to which this knowledge can be transferred to date new sentences. The learned year embeddings show a strong linear correlation between their first principal component and time. We show that temporal information encoded in the model can be used to predict novel sentences' years of composition relatively well. Comparisons to a feedforward baselin…
A Cookbook for Community-driven Data Collection of Impaired Speech in LowResource Languages
University of Ghana（加纳大学）；University of West London（西伦敦大学）；Talking Tipps Africa Foundation（Talking Tipps Africa基金会）；University College London（伦敦大学学院）；University of Oulu（奥卢大学）；Google Research（谷歌研究院）
We present our first efforts towards building a single multilingual automatic speech recognition (ASR) system that can process code-switching (CS) speech in five languages spoken within the same population. This contrasts with related prior work which focuses on the recognition of CS speech in bilingual scenarios. Recently, we have compiled a small five-language corpus of South African soap opera speech which contains examples of CS between 5 languages occurring in various contexts such as using English as the matrix language and switching to other indigenous languages. The ASR system presente…
In this paper, we describe recent performance improvements to the production Marchex speech recognition system for our spontaneous customer-to-business telephone conversations. In our previous work, we focused on in-domain language and acoustic model training. In this work we employ state-of-the-art semi-supervised lattice-free maximum mutual information (LF-MMI) training process which can supervise over full lattices from unlabeled audio. On Marchex English (ME), a modern evaluation set of conversational North American English, we observed a 3.3% (3.2% for agent, 3.6% for caller) reduction in…
Indian Institute of Science Education and Research (IISER) Berhampur（贝兰布尔印度科学教育与研究院（IISER））
Automated Speech Recognition (ASR) is an interdisciplinary application of computer science and linguistics that enable us to derive the transcription from the uttered speech waveform. It finds several applications in Military like High-performance fighter aircraft, helicopters, air-traffic controller. Other than military speech recognition is used in healthcare, persons with disabilities and many more. ASR has been an active research area. Several models and algorithms for speech to text (STT) have been proposed. One of the most recent is Mozilla Deep Speech, it is based on the Deep Speech res…
Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学信息与语言处理中心（CIS））
To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis. When it comes to analysis of bias, previous work has focused predominantly on race. In our work, we further investigate bias in hate speech datasets along racial, gender and intersectional axes. We identify strong bias against African American English (AAE), masculine and AAE+Masculine tweets, which are annotated as disproportionately more hateful and offensive than from other demographics. We provide evidence that BERT-based models propagate this bias and show that balancing the training…
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR
Mila Quebec AI Institute（米拉魁北克人工智能研究所）；Lanfrica；Lelapa AI；Instituto Politécnico Nacional（国立理工学院）；Masakhane NLP（Masakhane自然语言处理组织）；Intron Health（Intron健康公司）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；AI Saturdays Lagos（拉各斯AI周六组织）；Karya Inc（Karya公司）；Ford Motor Company（福特汽车公司）；McGill University（麦吉尔大学）；University of Deutso（德乌措大学）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；University of Minnesota（明尼苏达大学）
针对非洲口音临床与通用 ASR 数据及基准缺失问题，本文发布涵盖 13 国 120 种口音的 200 小时 AfriSpeech 数据集、测试基准及 SOTA 预训练模型。
Maseno University（马塞诺大学）；University of Cape Town（开普敦大学）；African Institute for Mathematical Sciences - AIMS（非洲数学科学研究所）；United States International University-Africa（美国国际大学非洲分校）；Zindi（津迪平台）；Jomo Kenyatta University of Agriculture and Technology - JKUAT（乔莫·肯雅塔农业技术大学）；The University of Minnesota, Twin Cities（明尼苏达大学双城分校）；University of Manchester（曼彻斯特大学）；Songhai（松海机构）；University of Groningen（格罗宁根大学）；Stellenboch University（斯泰伦博斯大学）；Jumia Group（朱米亚集团）；Adventist University of Africa（非洲基督复临大学）；University of Nairobi（内罗毕大学）；Deep Learning Indaba（深度学习印达巴组织）；Karadeniz Technical University（卡拉德尼兹技术大学）；Datawise Africa（非洲数据智能机构）；Kisii University（基西大学）；Future Africa - University of Pretoria（比勒陀利亚大学未来非洲中心）
